无线通信发射机和接收机的频道不可知训练
Christopher P Davey1, Ismail Shakeel2, Ravinesh C Deo1
1School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了一种更简单的方法来训练无线通信系统,而不需要道模型. 新方法的性能与现有的复杂方法相美,提高了在动态环境中的适应性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的无线系统使用数学模型独立优化组件.
- 深度学习可以实现端到端的优化,但需要一个假设的道模型进行培训.
- 现有的空中训练方法用于分离的发射机/接收机训练具有诸如复杂性或对错误的敏感性等缺点.
研究的目的:
- 提出一种更简单,更有效的方法,用于无线通信系统的分离训练.
- 为了使发射器和接收器在不依赖假设的通道模型的情况下进行训练.
- 为了实现与端到端培训相匹配的性能,同时提高适应能力.
主要方法:
- 开发了一种新的方法,局部接收器模型与远程接收器模型相近.
- 利用近似的本地接收器模型来训练本地发射器.
- 在各种道条件下评估拟议的方法.
主要成果:
- 简化的分离训练方法在不同的道条件下表现出强的表现.
- 达到与传统端到端培训方法相当的性能.
- 该方法非常适合适应不断变化的道环境.
结论:
- 拟议的简化分离培训方法为复杂的现有技术提供了有效的替代方案.
- 这种方法为训练无线通信系统提供了更具适应性和效率的解决方案.
- 进一步的研究可以探索其在多样化和动态的通信场景中的应用.
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